Multi-view learning (MVL) has attracted increasing attention and achieved great practical
success by exploiting complementary information of multiple features or modalities …

Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

This paper presents ClusterFormer, a universal vision model that is based on the Clustering
paradigm with TransFormer. It comprises two novel designs: 1) recurrent cross-attention …

In the big data era, the data are generated from different sources or observed from different
views. These data are referred to as multi-view data. Unleashing the power of knowledge in …

In multiview learning, it is essential to assign a reasonable weight to each view according to
the view importance. Thus, for multiview clustering task, a wise and elegant method should …

Previous multi-view clustering algorithms mostly partition the multi-view data in their original
feature space, the efficacy of which heavily and implicitly relies on the quality of the original …

Most existing graph-based clustering methods need a predefined graph and their clustering
performance highly depends on the quality of the graph. Aiming to improve the multiview …

Due to the efficiency of learning relationships and complex structures hidden in data, graph-
oriented methods have been widely investigated and achieve promising performance in …

Graph-based approaches have been successful in unsupervised and semi-supervised
learning. In this paper, we focus on the real-world applications where the same instance can …

Due to the efficiency of learning relationships and complex structures hidden in data, graph-
oriented methods have been widely investigated and achieve promising performance …